Data-driven model for hydraulic fracturing design optimization. Part II: Inverse problem

@article{Duplyakov2021DatadrivenMF,
  title={Data-driven model for hydraulic fracturing design optimization. Part II: Inverse problem},
  author={Viktor Duplyakov and A. Morozov and D. A. Popkov and E. V. Shel and A. L. Vainshtein and Evgeny V. Burnaev and Andrei A. Osiptsov and Grigory Paderin},
  journal={ArXiv},
  year={2021},
  volume={abs/2108.00751}
}
We describe a stacked model for predicting the cumulative fluid production for an oil well with a multistage-fracture completion based on a combination of Ridge Regression and CatBoost algorithms. The model is developed based on an extended digital field data base of reservoir, well and fracturing design parameters. The database now includes more than 5000 wells from 23 oilfields of Western Siberia (Russia), with 6687 fracturing operations in total. Starting with 387 parameters characterizing… 
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